@inproceedings{11816,
  abstract     = {{In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.}},
  author       = {{Hoang, Manh Kha and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Gaussian processes, Global Positioning System, convergence, expectation-maximisation algorithm, fingerprint identification, indoor radio, signal classification, wireless LAN, EM algorithm, ML estimation, WiFi indoor positioning, censored Gaussian data classification, clipped data, convergence properties, expectation maximization algorithm, fingerprinting method, maximum likelihood estimation, optimal classification, parameters estimation, portable devices sensitivity, signal strength measurements, wireless LAN positioning systems, Convergence, IEEE 802.11 Standards, Maximum likelihood estimation, Parameter estimation, Position measurement, Training, Indoor positioning, censored data, expectation maximization, signal strength, wireless LAN}},
  pages        = {{3721--3725}},
  title        = {{{Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}}},
  doi          = {{10.1109/ICASSP.2013.6638353}},
  year         = {{2013}},
}

@inproceedings{38104,
  abstract     = {{Location-aware services for private use such as GPS-
based navigation systems and GSM-based offerings
have become quite a success for outdoor applications,
while indoor positioning systems are still mainly
employed for professional use only. The main reasons
are cost issues and the complexity of setup and
maintenance of those systems. In this paper we
present CaMPTrack (Camera-based Multiple Person
Tracker), a prototype of a webcam-based positioning
system and discuss its application and development
challenges.}},
  author       = {{Schäfer, Robbie and Müller, Wolfgang and Deimann, Roman and Kleinjohann, Bernd}},
  booktitle    = {{Proceedings of the Workshop on Mobile Spatial Interaction at CHI 2007}},
  keywords     = {{Positioning Systems, Camera Based, Cost Efficiency, Smart Home Applications}},
  title        = {{{A Low-Cost Positioning System for Location-Aware Applications in Smart Homes}}},
  year         = {{2007}},
}